Checkpointing a set of stream computing data

ABSTRACT

Disclosed aspects relate to checkpointing a set of stream computing data with respect to a stream computing environment having a set of windowed stream operators including both a first windowed stream operator and a second windowed stream operator. It may be identified that the first windowed stream operator has a first subset of the set of stream computing data. It may be identified that the second windowed stream operator has the first subset of the set of stream computing data. It may be determined to checkpoint the first subset of the set of stream computing data without a redundant checkpoint related to the first and second windowed stream operators. The set of stream computing data may be checkpointed without the redundant checkpoint of the first subset of the set of stream computing data.

BACKGROUND

This disclosure relates generally to computer systems and, moreparticularly, relates to checkpointing a set of stream computing datawith respect to a stream computing environment having a set of windowedstream operators including both a first windowed stream operator and asecond windowed stream operator. The amount of data that needs to bemanaged is increasing. As data needing to be managed increases, the needfor checkpointing a set of stream computing data with respect to astream computing environment having a set of windowed stream operatorsincluding both a first windowed stream operator and a second windowedstream operator may also increase.

SUMMARY

Aspects of the disclosure relate to checkpointing a set of streamcomputing data with respect to a stream computing environment having aset of windowed stream operators. A set of stream computing data that isshared between multiple stream operators may be checkpointed tofacilitate non-redundant data storage. A set of windowed operators of astream computing environment that share a set of stream computing datamay be detected. The shared set of stream computing data may becheckpointed for the set of windowed operators in a non-redundantfashion. Checkpointing operations may be divided between multipleoperators of the set of windowed operators to balance the bandwidth andcheckpointing workload of the set of windowed operators. Streamcomputing applications may be compiled to use a shared memory withrespect to the set of windowed operators to facilitate datacheckpointing. Checkpointing operations may be performed based on thethroughput characteristics, resource availability, congestion,performance, and other factors related to the set of windowed streamoperators. Checkpointed data may be retrieved to rebuild the state ofthe window for one or more operators of the set of windowed operators.

Disclosed aspects relate to checkpointing a set of stream computing datawith respect to a stream computing environment having a set of windowedstream operators including both a first windowed stream operator and asecond windowed stream operator. It may be identified that the firstwindowed stream operator has a first subset of the set of streamcomputing data. It may be identified that the second windowed streamoperator has the first subset of the set of stream computing data. Itmay be determined to checkpoint the first subset of the set of streamcomputing data without a redundant checkpoint related to the first andsecond windowed stream operators. The set of stream computing data maybe checkpointed without the redundant checkpoint of the first subset ofthe set of stream computing data.

The above summary is not intended to describe each illustratedembodiment or every implementation of the present disclosure.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The drawings included in the present application are incorporated into,and form part of, the specification. They illustrate embodiments of thepresent disclosure and, along with the description, serve to explain theprinciples of the disclosure. The drawings are only illustrative ofcertain embodiments and do not limit the disclosure.

FIG. 1 illustrates an exemplary computing infrastructure to execute astream computing application according to embodiments.

FIG. 2 illustrates a view of a compute node according to embodiments.

FIG. 3 illustrates a view of a management system according toembodiments.

FIG. 4 illustrates a view of a compiler system according to embodiments.

FIG. 5 illustrates an exemplary operator graph for a stream computingapplication according to embodiments.

FIG. 6 is a flowchart illustrating a method for checkpointing a set ofstream computing data with respect to a stream computing environmenthaving a set of windowed stream operators including both a firstwindowed stream operator and a second windowed stream operator,according to embodiments.

FIG. 7 is a flowchart illustrating a method for checkpointing a set ofstream computing data with respect to a stream computing environmenthaving a set of windowed stream operators including both a firstwindowed stream operator and a second windowed stream operator,according to embodiments.

FIG. 8 is a flowchart illustrating a method for checkpointing a set ofstream computing data with respect to a stream computing environmenthaving a set of windowed stream operators including both a firstwindowed stream operator and a second windowed stream operator,according to embodiments.

FIG. 9 is a flowchart illustrating a method for checkpointing a set ofstream computing data with respect to a stream computing environmenthaving a set of windowed stream operators including both a firstwindowed stream operator and a second windowed stream operator,according to embodiments.

FIG. 10 illustrates an example system for checkpointing a set of streamcomputing data with respect to a stream computing environment having aset of windowed stream operators including both a first windowed streamoperator and a second windowed stream operator, according toembodiments.

FIG. 11 is a flowchart illustrating a method for checkpointing a set ofstream computing data with respect to a stream computing environmenthaving a set of windowed stream operators including both a firstwindowed stream operator and a second windowed stream operator,according to embodiments.

FIG. 12 illustrates an example for checkpointing a set of streamcomputing data with respect to a stream computing environment having aset of windowed stream operators including both a first windowed streamoperator and a second windowed stream operator, according toembodiments.

FIG. 13 illustrates an example for checkpointing a set of streamcomputing data with respect to a stream computing environment having aset of windowed stream operators including both a first windowed streamoperator and a second windowed stream operator, according toembodiments.

FIG. 14 illustrates an example for checkpointing a set of streamcomputing data with respect to a stream computing environment having aset of windowed stream operators including both a first windowed streamoperator and a second windowed stream operator, according toembodiments.

While the invention is amenable to various modifications and alternativeforms, specifics thereof have been shown by way of example in thedrawings and will be described in detail. It should be understood,however, that the intention is not to limit the invention to theparticular embodiments described. On the contrary, the intention is tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the invention.

DETAILED DESCRIPTION

Aspects of the disclosure relate to checkpointing a set of streamcomputing data with respect to a stream computing environment having aset of windowed stream operators. A set of stream computing data that isshared between multiple stream operators (e.g., stored on separatehosts) may be checkpointed to facilitate non-redundant data storage. Aset of windowed operators of a stream computing environment that share aset of stream computing data may be detected. The shared set of streamcomputing data may be checkpointed for the set of windowed operators ina non-redundant fashion (e.g., such that overlapping data is notcheckpointed multiple times). Checkpointing operations may be dividedbetween multiple operators of the set of windowed operators to balancethe bandwidth and checkpointing workload of the set of windowedoperators. Stream computing applications may be compiled to use a sharedmemory with respect to the set of windowed operators to facilitate datacheckpointing. Checkpointing operations may be performed based on thethroughput characteristics, resource availability, congestion,performance, and other factors related to the set of windowed streamoperators. Checkpointed data may be retrieved to rebuild the state ofthe window for one or more operators of the set of windowed operators.Altogether, leveraging non-redundant checkpointing with respect toshared data of a set of windowed stream operators may be associated withbenefits such as data storage efficiency, bandwidth, and streamcomputing application performance.

In stream computing environments, checkpointing data maintained instream operator windows is one technique used to facilitate datasecurity and integrity. Aspects of the disclosure relate to therecognition that, in some situations, a plurality of stream operatorsthat share the same set of stream computing data may each performcheckpointing operations, resulting in redundant data storage as thesame set of stream computing data is stored in memory multiple times.Accordingly, aspects of the disclosure relate to identifying streamoperators that share stream computing data, and checkpointing the streamcomputing data in a non-redundant fashion (e.g., by designating one ormore stream operators to manage checkpointing, dividing checkpointoperations between operators). As such, checkpointing of streamcomputing data may be performed to promote data storage flexibility,data back-up reliability, and system resource usage efficiency in astream computing environment.

Stream-based computing and stream-based database computing are emergingas a developing technology for database systems. Products are availablewhich allow users to create applications that process and querystreaming data before it reaches a database file. With this emergingtechnology, users can specify processing logic to apply to inbound datarecords while they are “in flight,” with the results available in a veryshort amount of time, often in fractions of a second. Constructing anapplication using this type of processing has opened up a newprogramming paradigm that will allow for development of a broad varietyof innovative applications, systems, and processes, as well as presentnew challenges for application programmers and database developers.

In a stream computing application, stream operators are connected to oneanother such that data flows from one stream operator to the next (e.g.,over a TCP/IP socket). When a stream operator receives data, it mayperform operations, such as analysis logic, which may change the tupleby adding or subtracting attributes, or updating the values of existingattributes within the tuple. When the analysis logic is complete, a newtuple is then sent to the next stream operator. Scalability is achievedby distributing an application across nodes by creating executables(i.e., processing elements), as well as replicating processing elementson multiple nodes and load balancing among them. Stream operators in astream computing application can be fused together to form a processingelement that is executable. Doing so allows processing elements to sharea common process space, resulting in much faster communication betweenstream operators than is available using inter-process communicationtechniques (e.g., using a TCP/IP socket). Further, processing elementscan be inserted or removed dynamically from an operator graphrepresenting the flow of data through the stream computing application.In some cases a particular stream operator may not reside within thesame operating system process as other stream operators. In addition,stream operators in the same operator graph may be hosted on differentnodes, e.g., on different compute nodes or on different cores of acompute node.

Data flows from one stream operator to another in the form of a “tuple.”A tuple is a sequence of one or more attributes associated with anentity. Attributes may be any of a variety of different types, e.g.,integer, float, Boolean, string, map, list, etc. The attributes may beordered. In addition to attributes associated with an entity, a tuplemay include metadata, i.e., data about the tuple. A tuple may beextended by adding one or more additional attributes or metadata to it.As used herein, “stream” or “data stream” refers to a sequence oftuples. Generally, a stream may be considered a pseudo-infinite sequenceof tuples.

Tuples are received and output by stream operators and processingelements. An input tuple corresponding with a particular entity that isreceived by a stream operator or processing element, however, isgenerally not considered to be the same tuple that is output by thestream operator or processing element, even if the output tuplecorresponds with the same entity or data as the input tuple. An outputtuple need not be changed in some way from the input tuple.

Nonetheless, an output tuple may be changed in some way by a streamoperator or processing element. An attribute or metadata may be added,deleted, or modified. For example, a tuple will often have two or moreattributes. A stream operator or processing element may receive thetuple having multiple attributes and output a tuple corresponding withthe input tuple. The stream operator or processing element may onlychange one of the attributes so that all of the attributes of the outputtuple except one are the same as the attributes of the input tuple.

Generally, a particular tuple output by a stream operator or processingelement may not be considered to be the same tuple as a correspondinginput tuple even if the input tuple is not changed by the processingelement. However, to simplify the present description and the claims, anoutput tuple that has the same data attributes or is associated with thesame entity as a corresponding input tuple will be referred to herein asthe same tuple unless the context or an express statement indicatesotherwise.

Stream computing applications handle massive volumes of data that needto be processed efficiently and in real time. For example, a streamcomputing application may continuously ingest and analyze hundreds ofthousands of messages per second and up to petabytes of data per day.Accordingly, each stream operator in a stream computing application maybe required to process a received tuple within fractions of a second.Unless the stream operators are located in the same processing element,it is necessary to use an inter-process communication path each time atuple is sent from one stream operator to another. Inter-processcommunication paths can be a critical resource in a stream computingapplication. According to various embodiments, the available bandwidthon one or more inter-process communication paths may be conserved.Efficient use of inter-process communication bandwidth can speed upprocessing.

A streams processing job has a directed graph of processing elementsthat send data tuples between the processing elements. The processingelement operates on the incoming tuples, and produces output tuples. Aprocessing element has an independent processing unit and runs on ahost. The streams platform can be made up of a collection of hosts thatare eligible for processing elements to be placed upon. When a job issubmitted to the streams run-time, the platform scheduler processes theplacement constraints on the processing elements, and then determines(the best) one of these candidates host for (all) the processingelements in that job, and schedules them for execution on the decidedhost.

Aspects of the disclosure relate to a system, method, and computerprogram product for checkpointing a set of stream computing data withrespect to a stream computing environment having a set of windowedstream operators including both a first windowed stream operator and asecond windowed stream operator. It may be identified that the firstwindowed stream operator has a first subset of the set of streamcomputing data. It may be identified that the second windowed streamoperator has the first subset of the set of stream computing data. Itmay be determined to checkpoint the first subset of the set of streamcomputing data without a redundant checkpoint related to the first andsecond windowed stream operators. The set of stream computing data maybe checkpointed without the redundant checkpoint of the first subset ofthe set of stream computing data.

In embodiments, the first windowed stream operator may be used tocheckpoint the first subset of the set of stream computing data and asecond windowed stream operator may be prevented from checkpointing thefirst subset of the set of stream computing data. In embodiments, it maybe determined to checkpoint the first subset of the set of streamcomputing data in response to compiling a stream computing applicationwith respect to the stream computing environment. In embodiments, it maybe determined to checkpoint the first subset of the set of streamcomputing data in advance of compiling a stream computing applicationwith respect to the stream computing environment. In embodiments, astream computing application may be compiled to use a shared memory forthe first subset of the set of stream computing data. In embodiments, itmay be detected that the first windowed stream operator is hosted by afirst compute node and that the second windowed stream operator ishosted by a second compute node. Altogether, aspects of the disclosurecan have performance or efficiency benefits. Aspects may save resourcessuch as bandwidth, disk, processing, or memory.

FIG. 1 illustrates one exemplary computing infrastructure 100 that maybe configured to execute a stream computing application, according tosome embodiments. The computing infrastructure 100 includes a managementsystem 105 and two or more compute nodes 110A-110D—i.e., hosts—which arecommunicatively coupled to each other using one or more communicationsnetworks 120. The communications network 120 may include one or moreservers, networks, or databases, and may use a particular communicationprotocol to transfer data between the compute nodes 110A-110D. Acompiler system 102 may be communicatively coupled with the managementsystem 105 and the compute nodes 110 either directly or via thecommunications network 120.

The communications network 120 may include a variety of types ofphysical communication channels or “links.” The links may be wired,wireless, optical, or any other suitable media. In addition, thecommunications network 120 may include a variety of network hardware andsoftware for performing routing, switching, and other functions, such asrouters, switches, or bridges. The communications network 120 may bededicated for use by a stream computing application or shared with otherapplications and users. The communications network 120 may be any size.For example, the communications network 120 may include a single localarea network or a wide area network spanning a large geographical area,such as the Internet. The links may provide different levels ofbandwidth or capacity to transfer data at a particular rate. Thebandwidth that a particular link provides may vary depending on avariety of factors, including the type of communication media andwhether particular network hardware or software is functioning correctlyor at full capacity. In addition, the bandwidth that a particular linkprovides to a stream computing application may vary if the link isshared with other applications and users. The available bandwidth mayvary depending on the load placed on the link by the other applicationsand users. The bandwidth that a particular link provides may also varydepending on a temporal factor, such as time of day, day of week, day ofmonth, or season.

FIG. 2 is a more detailed view of a compute node 110, which may be thesame as one of the compute nodes 110A-110D of FIG. 1, according tovarious embodiments. The compute node 110 may include, withoutlimitation, one or more processors (CPUs) 205, a network interface 215,an interconnect 220, a memory 225, and a storage 230. The compute node110 may also include an I/O device interface 210 used to connect I/Odevices 212, e.g., keyboard, display, and mouse devices, to the computenode 110.

Each CPU 205 retrieves and executes programming instructions stored inthe memory 225 or storage 230. Similarly, the CPU 205 stores andretrieves application data residing in the memory 225. The interconnect220 is used to transmit programming instructions and application databetween each CPU 205, I/O device interface 210, storage 230, networkinterface 215, and memory 225. The interconnect 220 may be one or morebusses. The CPUs 205 may be a single CPU, multiple CPUs, or a single CPUhaving multiple processing cores in various embodiments. In oneembodiment, a processor 205 may be a digital signal processor (DSP). Oneor more processing elements 235 (described below) may be stored in thememory 225. A processing element 235 may include one or more streamoperators 240 (described below). In one embodiment, a processing element235 is assigned to be executed by only one CPU 205, although in otherembodiments the stream operators 240 of a processing element 235 mayinclude one or more threads that are executed on two or more CPUs 205.The memory 225 is generally included to be representative of a randomaccess memory, e.g., Static Random Access Memory (SRAM), Dynamic RandomAccess Memory (DRAM), or Flash. The storage 230 is generally included tobe representative of a non-volatile memory, such as a hard disk drive,solid state device (SSD), or removable memory cards, optical storage,flash memory devices, network attached storage (NAS), or connections tostorage area network (SAN) devices, or other devices that may storenon-volatile data. The network interface 215 is configured to transmitdata via the communications network 120.

A stream computing application may include one or more stream operators240 that may be compiled into a “processing element” container 235. Thememory 225 may include two or more processing elements 235, eachprocessing element having one or more stream operators 240. Each streamoperator 240 may include a portion of code that processes tuples flowinginto a processing element and outputs tuples to other stream operators240 in the same processing element, in other processing elements, or inboth the same and other processing elements in a stream computingapplication. Processing elements 235 may pass tuples to other processingelements that are on the same compute node 110 or on other compute nodesthat are accessible via communications network 120. For example, aprocessing element 235 on compute node 110A may output tuples to aprocessing element 235 on compute node 110B.

The storage 230 may include a buffer 260. Although shown as being instorage, the buffer 260 may be located in the memory 225 of the computenode 110 or in a combination of both memories. Moreover, storage 230 mayinclude storage space that is external to the compute node 110, such asin a cloud.

The compute node 110 may include one or more operating systems 262. Anoperating system 262 may be stored partially in memory 225 and partiallyin storage 230. Alternatively, an operating system may be storedentirely in memory 225 or entirely in storage 230. The operating systemprovides an interface between various hardware resources, including theCPU 205, and processing elements and other components of the streamcomputing application. In addition, an operating system provides commonservices for application programs, such as providing a time function.

FIG. 3 is a more detailed view of the management system 105 of FIG. 1according to some embodiments. The management system 105 may include,without limitation, one or more processors (CPUs) 305, a networkinterface 315, an interconnect 320, a memory 325, and a storage 330. Themanagement system 105 may also include an I/O device interface 310connecting I/O devices 312, e.g., keyboard, display, and mouse devices,to the management system 105.

Each CPU 305 retrieves and executes programming instructions stored inthe memory 325 or storage 330. Similarly, each CPU 305 stores andretrieves application data residing in the memory 325 or storage 330.The interconnect 320 is used to move data, such as programminginstructions and application data, between the CPU 305, I/O deviceinterface 310, storage unit 330, network interface 315, and memory 325.The interconnect 320 may be one or more busses. The CPUs 305 may be asingle CPU, multiple CPUs, or a single CPU having multiple processingcores in various embodiments. In one embodiment, a processor 305 may bea DSP. Memory 325 is generally included to be representative of a randomaccess memory, e.g., SRAM, DRAM, or Flash. The storage 330 is generallyincluded to be representative of a non-volatile memory, such as a harddisk drive, solid state device (SSD), removable memory cards, opticalstorage, Flash memory devices, network attached storage (NAS),connections to storage area-network (SAN) devices, or the cloud. Thenetwork interface 315 is configured to transmit data via thecommunications network 120.

The memory 325 may store a stream manager 134. Additionally, the storage330 may store an operator graph 335. The operator graph 335 may definehow tuples are routed to processing elements 235 (FIG. 2) for processingor stored in memory 325 (e.g., completely in embodiments, partially inembodiments).

The management system 105 may include one or more operating systems 332.An operating system 332 may be stored partially in memory 325 andpartially in storage 330. Alternatively, an operating system may bestored entirely in memory 325 or entirely in storage 330. The operatingsystem provides an interface between various hardware resources,including the CPU 305, and processing elements and other components ofthe stream computing application. In addition, an operating systemprovides common services for application programs, such as providing atime function.

FIG. 4 is a more detailed view of the compiler system 102 of FIG. 1according to some embodiments. The compiler system 102 may include,without limitation, one or more processors (CPUs) 405, a networkinterface 415, an interconnect 420, a memory 425, and storage 430. Thecompiler system 102 may also include an I/O device interface 410connecting I/O devices 412, e.g., keyboard, display, and mouse devices,to the compiler system 102.

Each CPU 405 retrieves and executes programming instructions stored inthe memory 425 or storage 430. Similarly, each CPU 405 stores andretrieves application data residing in the memory 425 or storage 430.The interconnect 420 is used to move data, such as programminginstructions and application data, between the CPU 405, I/O deviceinterface 410, storage unit 430, network interface 415, and memory 425.The interconnect 420 may be one or more busses. The CPUs 405 may be asingle CPU, multiple CPUs, or a single CPU having multiple processingcores in various embodiments. In one embodiment, a processor 405 may bea DSP. Memory 425 is generally included to be representative of a randomaccess memory, e.g., SRAM, DRAM, or Flash. The storage 430 is generallyincluded to be representative of a non-volatile memory, such as a harddisk drive, solid state device (SSD), removable memory cards, opticalstorage, flash memory devices, network attached storage (NAS),connections to storage area-network (SAN) devices, or to the cloud. Thenetwork interface 415 is configured to transmit data via thecommunications network 120.

The compiler system 102 may include one or more operating systems 432.An operating system 432 may be stored partially in memory 425 andpartially in storage 430. Alternatively, an operating system may bestored entirely in memory 425 or entirely in storage 430. The operatingsystem provides an interface between various hardware resources,including the CPU 405, and processing elements and other components ofthe stream computing application. In addition, an operating systemprovides common services for application programs, such as providing atime function.

The memory 425 may store a compiler 136. The compiler 136 compilesmodules, which include source code or statements, into the object code,which includes machine instructions that execute on a processor. In oneembodiment, the compiler 136 may translate the modules into anintermediate form before translating the intermediate form into objectcode. The compiler 136 may output a set of deployable artifacts that mayinclude a set of processing elements and an application descriptionlanguage file (ADL file), which is a configuration file that describesthe stream computing application. In embodiments, a streams applicationbundle or streams application bundle file may be created. In someembodiments, the compiler 136 may be a just-in-time compiler thatexecutes as part of an interpreter. In other embodiments, the compiler136 may be an optimizing compiler. In various embodiments, the compiler136 may perform peephole optimizations, local optimizations, loopoptimizations, inter-procedural or whole-program optimizations, machinecode optimizations, or any other optimizations that reduce the amount oftime required to execute the object code, to reduce the amount of memoryrequired to execute the object code, or both. The output of the compiler136 may be represented by an operator graph, e.g., the operator graph335.

The compiler 136 may also provide the application administrator with theability to optimize performance through profile-driven fusionoptimization. Fusing operators may improve performance by reducing thenumber of calls to a transport. While fusing stream operators mayprovide faster communication between operators than is available usinginter-process communication techniques, any decision to fuse operatorsrequires balancing the benefits of distributing processing acrossmultiple compute processes with the benefit of faster inter-operatorcommunications. The compiler 136 may automate the fusion process todetermine how to best fuse the operators to be hosted by one or moreprocessing elements, while respecting user-specified constraints. Thismay be a two-step process, including compiling the application in aprofiling mode and running the application, then re-compiling and usingthe optimizer during this subsequent compilation. The end result may,however, be a compiler-supplied deployable application with an optimizedapplication configuration.

FIG. 5 illustrates an exemplary operator graph 500 for a streamcomputing application beginning from one or more sources 135 through toone or more sinks 504, 506, according to some embodiments. This flowfrom source to sink may also be generally referred to herein as anexecution path. In addition, a flow from one processing element toanother may be referred to as an execution path in various contexts.Although FIG. 5 is abstracted to show connected processing elementsPE1-PE10, the operator graph 500 may include data flows between streamoperators 240 (FIG. 2) within the same or different processing elements.Typically, processing elements, such as processing element 235 (FIG. 2),receive tuples from the stream as well as output tuples into the stream(except for a sink—where the stream terminates, or a source—where thestream begins). While the operator graph 500 includes a relatively smallnumber of components, an operator graph may be much more complex and mayinclude many individual operator graphs that may be statically ordynamically linked together.

The example operator graph shown in FIG. 5 includes ten processingelements (labeled as PE1-PE10) running on the compute nodes 110A-110D. Aprocessing element may include one or more stream operators fusedtogether to form an independently running process with its own processID (PID) and memory space. In cases where two (or more) processingelements are running independently, inter-process communication mayoccur using a “transport,” e.g., a network socket, a TCP/IP socket, orshared memory. Inter-process communication paths used for inter-processcommunications can be a critical resource in a stream computingapplication. However, when stream operators are fused together, thefused stream operators can use more rapid communication techniques forpassing tuples among stream operators in each processing element.

The operator graph 500 begins at a source 135 and ends at a sink 504,506. Compute node 110A includes the processing elements PE1, PE2, andPE3. Source 135 flows into the processing element PE1, which in turnoutputs tuples that are received by PE2 and PE3. For example, PE1 maysplit data attributes received in a tuple and pass some data attributesin a new tuple to PE2, while passing other data attributes in anothernew tuple to PE3. As a second example, PE1 may pass some received tuplesto PE2 while passing other tuples to PE3. Tuples that flow to PE2 areprocessed by the stream operators contained in PE2, and the resultingtuples are then output to PE4 on compute node 110B. Likewise, the tuplesoutput by PE4 flow to operator sink PE6 504. Similarly, tuples flowingfrom PE3 to PE5 also reach the operators in sink PE6 504. Thus, inaddition to being a sink for this example operator graph, PE6 could beconfigured to perform a join operation, combining tuples received fromPE4 and PE5. This example operator graph also shows tuples flowing fromPE3 to PE7 on compute node 110C, which itself shows tuples flowing toPE8 and looping back to PE7. Tuples output from PE8 flow to PE9 oncompute node 110D, which in turn outputs tuples to be processed byoperators in a sink processing element, for example PE10 506.

Processing elements 235 (FIG. 2) may be configured to receive or outputtuples in various formats, e.g., the processing elements or streamoperators could exchange data marked up as XML, documents. Furthermore,each stream operator 240 within a processing element 235 may beconfigured to carry out any form of data processing functions onreceived tuples, including, for example, writing to database tables orperforming other database operations such as data joins, splits, reads,etc., as well as performing other data analytic functions or operations.

The stream manager 134 of FIG. 1 may be configured to monitor a streamcomputing application running on compute nodes, e.g., compute nodes110A-110D, as well as to change the deployment of an operator graph,e.g., operator graph 132. The stream manager 134 may move processingelements from one compute node 110 to another, for example, to managethe processing loads of the compute nodes 110A-110D in the computinginfrastructure 100. Further, stream manager 134 may control the streamcomputing application by inserting, removing, fusing, un-fusing, orotherwise modifying the processing elements and stream operators (orwhat tuples flow to the processing elements) running on the computenodes 110A-110D.

Because a processing element may be a collection of fused streamoperators, it is equally correct to describe the operator graph as oneor more execution paths between specific stream operators, which mayinclude execution paths to different stream operators within the sameprocessing element. FIG. 5 illustrates execution paths betweenprocessing elements for the sake of clarity.

FIG. 6 is a flowchart illustrating a method 600 for checkpointing a setof stream computing data with respect to a stream computing environmenthaving a set of windowed stream operators including both a firstwindowed stream operator and a second windowed stream operator,according to embodiments. The stream computing environment may include aplatform for dynamically delivering and analyzing data in real-time. Thestream computing environment may include an operator graph having aplurality of stream operators (e.g., filter operations, sort operators,join operators) and processing elements configured to perform processingoperations on tuples flowing through the operator graph. In embodiments,the stream computing environment may include a set of windowed streamoperators. The set of windowed stream operators may include one or morestream operators of the stream computing environment having a window tofacilitate data analysis. Generally, the window may include a buffer orqueue configured to hold (e.g., maintain) a set of data in order toperform an analysis operation on the set of data. For instance, thewindow may be configured to hold data (e.g., tuples) over a particulartime period (e.g., tuples from the last 1 minute, 10 minutes, 4 hours),a specified number of tuples (e.g., 500 tuples, 1000 tuples), or adesignated capacity of data (e.g., 1 gigabyte, 5 gigabytes). Aspects ofthe disclosure relate to checkpointing a set of stream computing datafrom one or more windows of the set of windowed stream operators in anon-redundant fashion. The method 600 may begin at block 601.

In embodiments, the identifying, the identifying, the determining, thecheckpointing, and the other steps described herein may each beperformed in a dynamic fashion at block 604. The steps described hereinmay be performed in a dynamic fashion to streamline checkpointing of theset of stream computing data. For instance, the identifying, theidentifying, the determining, the checkpointing, and the other stepsdescribed herein may occur in real-time, ongoing, or on-the-fly. As anexample, one or more steps described herein may be performed inreal-time (e.g., sets of stream computing data may be dynamicallycheckpointed in response to identifying that multiple windowed streamoperators share the same subset of stream computing data) in order tostreamline (e.g., facilitate, promote, enhance) checkpointing of the setof stream computing data. Other methods of performing the stepsdescribed herein are also possible.

In embodiments, the identifying, the identifying, the determining, thecheckpointing, and the other steps described herein may each beperformed in an automated fashion at block 606. The steps describedherein may be performed in an automated fashion without userintervention. In embodiments, the identifying, the identifying, thedetermining, the checkpointing, and the other steps described herein maybe carried-out by an internal checkpointing management module maintainedin a persistent storage device of a local computing device (e.g.,network node). In embodiments, the identifying, the identifying, thedetermining, the checkpointing, and the other steps described herein maybe carried-out by an external configuration management module hosted bya remote computing device or server (e.g., server accessible via asubscription, usage-based, or other service model). In this way, aspectsof checkpointing the set of stream computing data may be performed usingautomated computing machinery without manual action. Other methods ofperforming the steps described herein are also possible.

At block 620, it may be identified that the first windowed streamoperator has a first subset of the set of stream computing data. Theidentifying may be performed with respect to the stream computingenvironment. Generally, identifying can include detecting, collecting,sensing, discovering, recognizing, distinguishing, or otherwiseascertaining that the first windowed stream operator has the firstsubset of the set of stream computing data. The first windowed streamoperator may include a stream operator associated with a window of thestream computing environment. For instance, the first windowed streamoperator may include a filter operator associated with a window thatstores incoming tuples prior to filtering. In embodiments, the firstwindowed stream operator may be associated with a dedicated window(e.g., window that stores data for exclusive use by the first windowedstream operator). In embodiments, the first windowed stream operator maybe associated with a shared window that maintains data for use bymultiple stream operators of the set of windows stream operators. Theset of stream computing data may include a collection of tuplesconfigured to undergo processing operations by one or more streamoperators of the operator graph. As described herein, the first windowedoperator may have a first subset of the set of stream computing data.The first subset of the set of stream computing data may include aportion, part, segment, or section of the set of stream computing datathat is associated with at least the first windowed stream operator. Forinstance, the first subset of the set of stream computing data mayinclude tuples that are marked for processing by the first windowedstream operator, have been processed by the first windowed streamoperator, are stored in a window communicatively connected to the firstwindowed stream operator, or the like. In embodiments, identifying thatthe first windowed stream operator has the first subset of the set ofstream computing data may include using a streams management engine toanalyze the set of windowed stream operators and determine that one ormore windowed stream operators are associated with the first subset ofthe set of stream computing data (e.g., similar data, overlapping data,identical data). For instance, the streams management engine mayidentify that a first subset of stream computing data including a set oftuples relating to temperature measurements are stored in a sharedwindow and marked for processing by the first windowed stream operator.Other methods of identifying that the first windowed stream operator hasthe first subset of the set of stream computing data are also possible.

At block 640, it may be identified that the second windowed streamoperator has the first subset of the set of stream computing data. Theidentifying may be performed with respect to the stream computingenvironment. Generally, identifying can include detecting, collecting,sensing, discovering, recognizing, distinguishing, or otherwiseascertaining that the second windowed stream operator has the firstsubset of the set of stream computing data. The second windowed streamoperator may include a stream operator associated with a window of thestream computing environment. For instance, the second windowed streamoperator may include a sort operator associated with a window thatstores incoming tuples prior to sorting. In embodiments, the secondwindowed stream operator may be associated with a dedicated window(e.g., window that stores data for exclusive use by the second windowedstream operator). In embodiments, the second windowed stream operatormay be associated with a shared window that maintains data for use bymultiple stream operators of the set of windows stream operators. Asdescribed herein, the second windowed operator may have the first subsetof the set of stream computing data. In embodiments, the first subset ofthe set of stream computing data may be substantially similar (e.g., thesame, identical, achieve a similarity threshold with respect to, overlapwith) between the first and second windowed stream operators (e.g.,multiple windowed stream operators have the same set of data). Forinstance, the first subset of the set of stream computing data mayinclude tuples that are maintained in a common window (e.g., queue)shared by both the first and second windowed stream operators. Inembodiments, identifying that the second windowed stream operator hasthe first subset of the set of stream computing data may include using astreams management engine to analyze the set of windowed streamoperators and determine that one or more windowed stream operators areassociated with the first subset of the set of stream computing data(e.g., similar data, overlapping data, identical data). For instance,with reference to the previous example, the streams management enginemay identify that the first subset of stream computing data includingthe set of tuples relating to temperature measurements are stored in ashared window and marked for processing by the second windowed streamoperator (e.g., as well as the first windowed stream operator). Othermethods of identifying that the second windowed stream operator has thefirst subset of the set of stream computing data are also possible.

At block 660, it may be determined to checkpoint the first subset of theset of stream computing data without a redundant checkpoint related tothe first and second windowed stream operators. The determining may beperformed based on both the first and second windowed stream operatorshaving the first subset of the set of stream computing data. Generally,determining can include computing, formulating, generating, calculating,selecting, identifying, or otherwise ascertaining to checkpoint thefirst subset of the set of stream computing data without a redundantcheckpoint related to the first and second windowed stream operators.The redundant checkpoint may include an operation to save, store, orretain stream computing data that has already been/will be/is currentlyin the process of being checkpointed (e.g., such that the same subset ofthe set of stream computing data is saved multiple times, resulting inexcess resource usage in terms of memory, bandwidth, and the like). Inembodiments, determining may include ascertaining that the first subsetof the set of stream computing data is substantially similar between thefirst and second windowed stream operators, and subsequently resolvingto checkpoint the first subset of the set of stream computing data in anon-redundant fashion. In embodiments, determining may includeformulating a checkpoint procedure that designates when and how thefirst subset of the set of stream computing data will be checkpointed(e.g., to prevent the first subset from being checkpointed multipletimes). As an example, determining may include designating a particularwindowed stream operator to perform the checkpoint operation (e.g., thefirst windowed stream operator) and scheduling a specific time period(e.g., 2:57 PM) for the first subset of the set of stream computing datato be checkpointed (e.g., such that the second windowed stream operatordoes not initiate a redundant checkpoint). Other methods of determiningto checkpoint the first subset of the set of stream computing datawithout a redundant checkpoint related to the first and second windowedstream operators are also possible.

In embodiments, throughput factors may be detected at block 661. A firstthroughput factor for the first windowed stream operator may bedetected. Generally, detecting can include sensing, discovering,collecting, recognizing, distinguishing, generating, obtaining,ascertaining, or otherwise determining the first throughput factor forthe first windowed stream operator. The first throughput factor mayinclude a quantitative or qualitative indication of the rate at whichdata is processed by/flows through the first windowed stream operator.In embodiments, detecting may include using a throughput diagnostic tomeasure the number of tuples processed by the first windowed streamoperator per unit time. As an example, a first throughput factor of “900tuples per second” may be determined for the first windowed streamoperator. In embodiments, a second throughput factor for the secondwindowed stream operator may be detected. The second throughput factormay include a quantitative or qualitative indication of the rate atwhich data is processed by/flows through the second windowed streamoperator. As an example, a second throughput factor of “700 tuples persecond” may be determined for the second windowed stream operator. Thefirst and second throughput factors for the first and second windowedstream operators may be compared. Generally, comparing can includecontrasting, analyzing, juxtaposing, correlating, or evaluating thefirst throughput factor with respect to the second throughput factor. Inembodiments, comparing can include examining the magnitude of the firstthroughput factor with respect to the magnitude of the second throughputfactor. As an example, the first throughput factor of “900 tuples persecond” may be compared with the second throughput factor of “700 tuplesper second.” It may be computed that the first throughput factor exceedsthe second throughput factor. Generally, computing can includeformulating, detecting, calculating, identifying, or otherwise resolvingthat the first throughput factor exceeds the second throughput factor.In embodiments, computing may include resolving that the magnitude ofthe first throughput factor is greater than the magnitude of the firstthroughput factor. With reference to the previous example, computing mayinclude determining that the first throughput factor of “900 tuples persecond” exceeds the first throughput factor of “700 tuples per second.”In embodiments, it may be determined to use the first windowed streamoperator to checkpoint the first subset of the set of stream computingdata. Generally, determining can include computing, formulating,generating, calculating, selecting, identifying, or otherwiseascertaining to use the first windowed stream operator to checkpoint thefirst subset of the set of stream computing data. The determining may beperformed based on the first throughput factor exceeding the secondthroughput factor. In embodiments, determining may include designatingthe first windowed stream operator to perform the checkpointingoperation with respect to the first subset of the set of streamcomputing data (e.g., as using stream operators with greater throughputfactors for checkpointing may be associated with operationalefficiency). Other methods of managing checkpoint based on throughputfactors for the set of windowed stream operators are also possible.

At block 680, the set of stream computing data may be checkpointed. Thecheckpointing may be performed without the redundant checkpoint of thefirst subset of the set of stream computing data related to the firstand second windowed stream operators. The checkpointing may be performedwith respect to the stream computing environment. Generally,checkpointing can include recording, saving, logging, preserving,storing, maintaining, or otherwise retaining the set of stream computingdata. Checkpointing may include capturing a copy (e.g., snapshot) of theset of stream computing data on a separate storage devicecommunicatively connected to the stream computing environment. Forinstance, checkpointing may include storing the set of stream computingdata in a cache, main system memory, hard disk drive, solid state drive,network attached storage (NAS) device, Redis server, or other storagedevice. As described herein, checkpointing may be performed without aredundant checkpoint of the first subset of the set of stream data. Inembodiments, checkpointing may include designating (e.g., selecting,nominating) one or more stream operators to store the set of streamcomputing data to a storage device (e.g., to prevent multiple streamoperators from storing the same data). As an example, consider a set ofstream computing data shared between three windowed operators A, B, andC. In certain embodiments, operator B may be designated to perform thecheckpointing operation to store the set of stream computing data (e.g.,such that A and C need not perform the checkpointing operation). Inembodiments, checkpointing may include dividing the checkpointingoperation among a plurality of stream operators, and instructing eachstream operator to checkpoint a non-overlapping portion of the set ofstream computing data (e.g., to facilitate bandwidth and workloadbalancing with respect to the set of windowed stream operators). Forinstance, with reference to the previous example, each of the windowedoperators A, B, and C may be configured to perform separate checkpointoperations to capture and store different portions of the set of streamcomputing data. Other methods of checkpointing the set of streamcomputing data without the redundant checkpoint are also possible.

In embodiments, the set of stream computing data may be checkpointed atblock 681. As described herein, in certain embodiments, aspects of thedisclosure relate to carrying-out checkpoint operations with respect tothe set of stream computing data using one or more designated streamoperators (e.g., to prevent redundant checkpointing operations). Inembodiments, the checkpointing may be performed using the first windowedstream operator to checkpoint the first subset of the set of streamcomputing data. The checkpointing may be performed with respect to thestream computing environment. Generally, checkpointing can includerecording, saving, logging, preserving, storing, maintaining, orotherwise retaining the first subset of the set of stream computing datausing the first windowed stream operator. In embodiments, checkpointingthe first subset of the set of stream computing data may includemodifying a set of checkpoint access permissions to authorize the firstwindowed stream operator to perform the checkpointing operation withrespect to the first subset of the set of stream computing data.Accordingly, the first windowed stream operator may be configured toperform the checkpointing operation by capturing a copy of the firstsubset of the set of stream computing data (e.g., from an associatedstream operator window), and writing the copy of the first subset of theset of stream computing data to a designated storage device. Inembodiments, the second windowed stream operator may be prevented fromcheckpointing the first subset of the set of stream computing data. Thepreventing may be performed with respect to the stream computingenvironment. Generally, preventing can include limiting, blocking,restricting, forbidding, controlling, or otherwise regulatingcheckpointing of the first subset of the set of stream computing data bythe second windowed stream operator. In embodiments, preventing mayinclude modifying a set of checkpoint access permissions to de-authorizethe second windowed stream operator from performing the checkpointingoperation with respect to the first subset of the set of streamcomputing data. In certain embodiments, preventing may include using astreams management engine to monitor a task manager for the streamcomputing environment, and canceling (e.g., blocking) any checkpointoperations initiated by the second windowed stream operator with respectto the first subset of the set of stream computing data. In this way,checkpointing may be performed with respect to the set of streamcomputing data without a redundant checkpoint. Other methods ofcheckpointing using the first windowed stream operator and preventingcheckpointing using the second windowed stream operator are alsopossible.

Consider the following example. A stream computing environment may beanalyzed using a streams management engine, and it may be identifiedthat four windowed stream operators A, B, C and D all share a firstsubset of stream computing data related to measured fuel economy valuesfor a vehicle. Based on all four windowed stream operators having thesame first subset of stream computing data, it may be determined tocheckpoint the first subset of the set of stream computing data. Inembodiments, throughput factors for each of the four windowed streamoperators may be examined, and it may be ascertained that streamoperator A has a throughput factor of 450 tuples per second, streamoperator B has a throughput factor of 790 tuples per second, throughputfactor C has a throughput factor of 210 tuples per second, and streamoperator D has a throughput factor of 1100 tuples per second. Asdescribed herein, in certain embodiments, a checkpointing procedure maybe determined for the first subset of stream computing data based on thethroughput factors for each stream operator. As an example, acheckpointing procedure may be determined that designates streamoperator D for performance of the checkpointing operation, as streamoperator D has the highest individual throughput factor of the fourstream operators. In certain embodiments, a checkpointing procedure maybe determined that assigns each stream operator a separate,non-overlapping portion of the stream computing data to checkpoint thatis proportional to the throughput rate of that stream operator relativeto the other windowed stream operators. For instance, stream operator Dmay be assigned a 43.1% portion of the first subset, stream operator Bmay be assigned a 31% portion of the first subset, stream operator A maybe assigned a 17.7% portion of the first subset, and stream operator Cmay be assigned a 8.2% portion of the first subset of the set of streamcomputing data for individual checkpointing (e.g., such that the entirefirst subset of the set of stream computing data is checkpointed withoutredundancy). Other methods of checkpointing a set of stream computingdata are also possible.

Method 600 concludes at block 699. Aspects of method 600 may provideperformance or efficiency benefits related to checkpointing a set ofstream computing data. As an example, checkpointing a set of streamcomputing data without a redundant checkpoint may save memory space andbandwidth, streamlining data security and integrity with respect to thestream computing environment. Altogether, leveraging non-redundantcheckpointing with respect to shared data of a set of windowed streamoperators may be associated with benefits such as data storageefficiency, bandwidth, and stream computing application performance.Aspects may save resources such as bandwidth, disk, processing, ormemory.

FIG. 7 is a flowchart illustrating a method 700 for checkpointing a setof stream computing data with respect to a stream computing environmenthaving a set of windowed stream operators including both a firstwindowed stream operator and a second windowed stream operator,according to embodiments. Aspects of the method 700 relate to managingcheckpointing of a set of stream computing data with respect to runtimeor compile-time of a stream computing application. Aspects of method 700may be similar or the same as method 600, and aspects may be utilizedinterchangeably. The method 700 may begin at block 701. At block 720, itmay be identified that the first windowed stream operator has a firstsubset of the set of stream computing data. The identifying may beperformed with respect to the stream computing environment. At block740, it may be identified that the second windowed stream operator hasthe first subset of the set of stream computing data. The identifyingmay be performed with respect to the stream computing environment.

In embodiments, it may be determined to checkpoint the first subset ofthe set of stream computing data at block 759. The determining may beperformed without the redundant checkpoint related to the first andsecond windowed stream operators. The determining may be performed inresponse to compiling a stream computing application with respect to thestream computing environment. Aspects of the disclosure relate to therecognition that, in some situations, it may be desirable to dynamicallycheckpoint stream computing data based on the real-time activity andperformance of a stream computing application. Accordingly, aspects ofthe disclosure relate to checkpointing the first subset of the set ofstream computing data in response to compilation of the stream computingapplication. Generally, determining can include computing, formulating,generating, calculating, selecting, identifying, or otherwiseascertaining to checkpoint the first subset of the set of streamcomputing data in response to compiling the stream computingapplication. In embodiments, determining to checkpoint the first subsetof the set of stream computing data may include resolving to checkpointthe first subset of the set of stream computing data based on run-timeperformance characteristics of the stream computing application. Forinstance, in certain embodiments, the streams management engine may beconfigured to run a series of performance diagnostics on the streamcomputing application to identify the throughput factors, window sizes,tuple flow rates, congestion levels, and other characteristics of theset of windowed stream operators, and subsequently ascertain one or morewindowed stream operators of the set of windowed stream operators toperform checkpointing based on the measured performance characteristics.For instance, windowed stream operators associated with greater tuplethroughput factors, larger window sizes, or lower congestion levels maybe prioritized for checkpointing operations (e.g., to facilitateefficient stream operating performance). Other methods of determining tocheckpoint the first subset of the set of stream computing data inresponse to compiling the stream computing application are alsopossible.

At block 760, it may be determined to checkpoint the first subset of theset of stream computing data without a redundant checkpoint related tothe first and second windowed stream operators. The determining may beperformed based on both the first and second windowed stream operatorshaving the first subset of the set of stream computing data.

In embodiments, it may be determined to checkpoint the first subset ofthe set of stream computing data at block 761. The determining may beperformed without the redundant checkpoint related to the first andsecond windowed stream operators. The determining may be performed inadvance of compiling a stream computing application with respect to thestream computing environment. Aspects of the disclosure relate to therecognition that, in some situations, un-compiled source code for astream computing application may be used to ascertain informationregarding the performance characteristics of windowed stream operators.Accordingly, aspects of the disclosure relate to determining whichstream operators may be used to perform checkpoint operations prior tocompiling the stream computing application. Generally, determining caninclude computing, formulating, generating, calculating, selecting,identifying, or otherwise ascertaining to checkpoint the first subset ofthe set of stream computing data in advance of compiling a streamcomputing application. In embodiments, determining may include examininga set of source code for the stream computing application, andidentifying a set of code components that correspond to windowed streamoperators (e.g., code that will implement operator functionality in thestream computing application after compilation). The set of codecomponents may be examined with respect to a set of projectedperformance criteria to detect a subset of the set of code componentsthat may be used for checkpointing operations in the stream computingapplication. As an example, in response to examining the set of sourcecode and identifying the set of code components, the set of codecomponents may be compared with a set of projected performance criteriathat define a threshold window size of “2000 tuples.” Accordingly, oneor more code components that achieve the threshold window size may bemarked or tagged for use to checkpoint the first subset of the set ofstream computing data. Other methods of determining to checkpoint thefirst subset of the set of stream computing data in advance of compilingthe stream computing application are also possible.

In embodiments, the stream computing application may be compiled atblock 762. The stream computing application may be compiled to use ashared memory. The compiling may be performed for the first subset ofthe set of stream computing data. The compiling may relate to the firstand second windowed stream operators. Generally, compiling can includeassembling, building, translating, aggregating, constructing,converting, or otherwise structuring the stream computing application touse the shared memory. The shared memory may include a memory resourcethat is configured for mutual access by the first and second windowedstream operators. For instance, the shared memory may include a set ofmemory addresses of a main system memory, a shared cache unit, adatabase, non-volatile memory unit (e.g., hard-drive, solid-statedrive), Redis server, or other type of memory resource configured foruse by at least the first and second windowed stream operators. Inembodiments, compiling the stream computing application to use theshared memory may include examining the source code of the streamcomputing application to identify a set of stream operator windows thatare projected to maintain an identical set of stream computing data, andsubsequently defining a shared memory space for use by the windowedstream operators corresponding to the identified windows. Consider thefollowing example. In response to examining the source code of anun-compiled stream computing application, it may be ascertained that afirst window corresponding to a first windowed stream operator and asecond window corresponding to a second windowed stream operator areconfigured to receive data from the same output (e.g., such that bothwill have identical sets of stream computing data). Accordingly, inembodiments, compiling may include modifying the set of source code todesignate a set of shared memory addresses for use by both the first andsecond windowed stream operators and subsequently converting the set ofsource code into a machine-executable application. In this way, the setof stream computing data may be saved (e.g., checkpointed) in the sharedmemory where it may be accessible by both the first and second windowedstream operators to facilitate data storage efficiency (e.g., only onecopy of the data need be saved instead of two, eliminating redundancy).Other methods of compiling the stream computing application to use ashared memory are also possible.

At block 780, the set of stream computing data may be checkpointed. Thecheckpointing may be performed without the redundant checkpoint of thefirst subset of the set of stream computing data related to the firstand second windowed stream operators. The checkpointing may be performedwith respect to the stream computing environment. Method 700 concludesat block 799. Aspects of method 700 may provide performance orefficiency benefits related to checkpointing a set of stream computingdata. Aspects may save resources such as bandwidth, disk, processing, ormemory.

FIG. 8 is a flowchart illustrating a method 800 for checkpointing a setof stream computing data with respect to a stream computing environmenthaving a set of windowed stream operators including both a firstwindowed stream operator and a second windowed stream operator,according to embodiments. Aspects of the method 800 relate to using aplurality of different windowed stream operators to checkpoint separateportions of the set of stream computing data. Aspects of method 800 maybe similar or the same as method 600/700, and aspects may be utilizedinterchangeably. The method 800 may begin at block 801. At block 820, itmay be identified that the first windowed stream operator has a firstsubset of the set of stream computing data. The identifying may beperformed with respect to the stream computing environment.

At block 825, it may be identified that the first windowed streamoperator has a second subset of the set of stream computing data. Theidentifying may be performed with respect to the stream computingenvironment. Generally, identifying can include detecting, collecting,sensing, discovering, recognizing, distinguishing, or otherwiseascertaining that the first windowed stream operator has the secondsubset of the set of stream computing data. As described herein, thefirst windowed stream operator may include a stream operator associatedwith a window of the stream computing environment. For instance, thefirst windowed stream operator may include a functor operator associatedwith a window that stores incoming tuples prior to processing. Inembodiments, the first windowed stream operator may have the secondsubset of the set of stream computing data. The second subset of the setof stream computing data may include a portion, part, segment, orsection of the set of stream computing data that is associated with atleast the first windowed stream operator. In embodiments, the secondsubset of the set of stream computing data may be different from thefirst subset (e.g., and the third subset) of the set of stream computingdata. For instance, the second subset may be mutually exclusive withrespect to the first subset (e.g., such that no tuple overlaps betweenthe first and second subsets). In embodiments, it may be identified thatthe first windowed stream operator does not have a third subset of theset of stream computing data at block 826. The identifying may beperformed with respect to the stream computing environment. The thirdsubset of the set of stream computing data may include a portion, part,segment, or section of the set of stream computing data that differsfrom (e.g., does not overlap with) the first or second subsets of theset of stream computing data. In embodiments, identifying that the firstwindowed stream operator has the second subset of the set of streamcomputing data and does not have the third subset may include using thestreams management engine to identify one or more windows associatedwith the first windowed stream operator, and examining the contents ofthe one or more windows to ascertain that the one or more windowsinclude tuples corresponding to the second subset of the set of streamcomputing data but not tuples corresponding to the third subset of theset of stream computing data. Other methods of identifying that thefirst windowed stream operator has the second subset of the set ofstream computing data and does not have the third subset of the set ofstream computing data are also possible.

At block 840, it may be identified that the second windowed streamoperator has the first subset of the set of stream computing data. Theidentifying may be performed with respect to the stream computingenvironment.

At block 845, it may be identified that the second windowed streamoperator has a third subset of the set of stream computing data. Theidentifying may be performed with respect to the stream computingenvironment. Generally, identifying can include detecting, collecting,sensing, discovering, recognizing, distinguishing, or otherwiseascertaining that the second windowed stream operator has the thirdsubset of the set of stream computing data. As described herein, thesecond windowed stream operator may include a stream operator associatedwith a window of the stream computing environment. For instance, thesecond windowed stream operator may include a barrier operatorassociated with a window that stores incoming tuples prior toprocessing. In embodiments, the second windowed stream operator may havethe third subset of the set of stream computing data. As describedherein, the third subset of the set of stream computing data may includea portion, part, segment, or section of the set of stream computing datathat is associated with at least the second windowed stream operator anddiffers from the first subset and the second subset of the set of streamcomputing data. For instance, the third subset may be mutually exclusivewith respect to the first and second subsets (e.g., such that no tupleoverlaps between the first, second and third subsets). In embodiments,it may be identified that the second windowed stream operator does nothave the second subset of the set of stream computing data at block 846.The identifying may be performed with respect to the stream computingenvironment. In embodiments, identifying that the second windowed streamoperator has the third subset of the set of stream computing data anddoes not have the second subset may include using the streams managementengine to identify one or more windows associated with the secondwindowed stream operator, and examining the contents of the one or morewindows to ascertain that the one or more windows include tuplescorresponding to the third subset of the set of stream computing databut not tuples corresponding to the second subset of the set of streamcomputing data. Other methods of identifying that the second windowedstream operator has the third subset of the set of stream computing dataand does not have the second subset of the set of stream computing dataare also possible.

At block 860, it may be determined to checkpoint the first subset of theset of stream computing data without a redundant checkpoint related tothe first and second windowed stream operators. The determining may beperformed based on both the first and second windowed stream operatorshaving the first subset of the set of stream computing data. At block880, the set of stream computing data may be checkpointed. Thecheckpointing may be performed without the redundant checkpoint of thefirst subset of the set of stream computing data related to the firstand second windowed stream operators. The checkpointing may be performedwith respect to the stream computing environment.

At block 885, the set of stream computing data may be checkpointed usingthe first windowed stream operator to checkpoint the second subset ofthe set of stream computing data. The checkpointing may be performedwith respect to the stream computing environment. Generally,checkpointing can include recording, saving, logging, preserving,storing, maintaining, or otherwise retaining the second subset of theset of stream computing data using the first windowed stream operator.As described herein, checkpointing may include capturing a copy (e.g.,snapshot) of the second subset of the set of stream computing data on aseparate storage device communicatively connected to the streamcomputing environment. In embodiments, checkpointing may include usingthe streams management engine to instruct the first windowed streamoperator to checkpoint the second subset of the set of stream computingdata by writing the second subset of the set of stream computing data toa designated memory location for storage. As an example, the firstwindowed stream operator may read the second subset from an associatedstream computing window, and subsequently write the second subset to aspecified set of memory addresses of system memory. Other methods ofcheckpointing the second subset of the set of stream computing datausing the first windowed stream operator are also possible.

At block 890, the set of stream computing data may be checkpointed usingthe second windowed stream operator to checkpoint the third subset ofthe set of stream computing data. The checkpointing may be performedwith respect to the stream computing environment. Generally,checkpointing can include recording, saving, logging, preserving,storing, maintaining, or otherwise retaining the third subset of the setof stream computing data using the second windowed stream operator. Asdescribed herein, checkpointing may include capturing a copy (e.g.,snapshot) of the third subset of the set of stream computing data on aseparate storage device communicatively connected to the streamcomputing environment. In embodiments, checkpointing may include usingthe streams management engine to schedule a checkpoint operation withrespect to the second windowed stream operator, such that the secondwindowed stream operator is configured to transmit the third subset ofthe set of stream computing data to a specified storage device at adesignated time. As an example, the streams management engine mayschedule a checkpoint operation for the second windowed stream operatorat a designated time of “11:15 AM,” such that the second windowed streamoperator is configured to transfer the third subset of the set of streamcomputing data to a communicatively connected network attached storagedevice at the designated time of “11:15 AM.” Other methods ofcheckpointing the third subset of the set of stream computing data usingthe third windowed stream operator are also possible.

Method 800 concludes at block 899. Aspects of method 800 may provideperformance or efficiency benefits related to checkpointing a set ofstream computing data. Altogether, leveraging non-redundantcheckpointing with respect to shared data of a set of windowed streamoperators may be associated with benefits such as data storageefficiency, bandwidth, and stream computing application performance.Aspects may save resources such as bandwidth, disk, processing, ormemory.

FIG. 9 is a flowchart illustrating a method 900 for checkpointing a setof stream computing data with respect to a stream computing environmenthaving a set of windowed stream operators including both a firstwindowed stream operator and a second windowed stream operator,according to embodiments. Aspects of method 900 may be similar or thesame as method 600/700/800, and aspects may be utilized interchangeably.The method 900 may begin at block 901. At block 920, it may beidentified that the first windowed stream operator has a first subset ofthe set of stream computing data. The identifying may be performed withrespect to the stream computing environment. At block 940, it may beidentified that the second windowed stream operator has the first subsetof the set of stream computing data. The identifying may be performedwith respect to the stream computing environment. At block 960, it maybe determined to checkpoint the first subset of the set of streamcomputing data without a redundant checkpoint related to the first andsecond windowed stream operators. The determining may be performed basedon both the first and second windowed stream operators having the firstsubset of the set of stream computing data. At block 980, the set ofstream computing data may be checkpointed. The checkpointing may beperformed without the redundant checkpoint of the first subset of theset of stream computing data related to the first and second windowedstream operators. The checkpointing may be performed with respect to thestream computing environment.

At block 986, the set of stream computing data may be checkpointed usingthe first windowed stream operator to checkpoint a first portion of thefirst subset of the set of stream computing data. The checkpointing maybe performed with respect to the stream computing environment.Generally, checkpointing can include recording, saving, logging,preserving, storing, maintaining, or otherwise retaining the set ofstream computing data using the first windowed stream operator tocheckpoint a first portion of the first subset of the set of streamcomputing data. The first portion may include a part, segment, section,or fraction of the first subset of the set of stream computing data. Inembodiments, the first portion may include a segment of the streamcomputing data that is exclusively used by (e.g., unique to, particularto) the first windowed stream operator (e.g., not shared by otherwindowed stream operators). In embodiments, the first portion may differfrom (e.g., not overlap with, be mutually exclusive with respect to) asecond portion of the first subset of the set of stream computing data.As an example, in a situation in which the set of stream computing datarelates to an Internet-of-Things environment, the first portion mayinclude a subset of the set of stream computing data that includestemperature measurements that are marked for filtering by a firstwindowed stream operator of a filter operator. In embodiments,checkpointing the first portion of the set of stream computing datausing the first windowed stream operator may include configuring thefirst windowed stream operator to parse the set of stream computing datato identify the first portion of the first subset of the set of streamcomputing data (e.g., tuples marked for processing by the first windowedstream operator), and subsequently transmitting the first portion to adesignated storage device to be maintained. Other methods ofcheckpointing the set of stream computing data using the first windowedstream operator to checkpoint a first portion of the first subset of theset of stream computing data are also possible.

In embodiments, the first windowed stream operator may be prevented fromcheckpointing the second portion of the first subset of the set ofstream computing data at block 987. The preventing may be performed withrespect to the stream computing environment. Generally, preventing caninclude limiting, blocking, restricting, forbidding, controlling, orotherwise regulating checkpointing of the first portion of the firstsubset of the set of stream computing data by the first windowed streamoperator. In embodiments, preventing may include modifying a set ofcheckpoint access permissions to de-authorize the first windowed streamoperator from performing the checkpointing operation with respect to asecond portion of the first subset of the set of stream computing data.In certain embodiments, preventing may include using a streamsmanagement engine to monitor a task manager for the stream computingenvironment, and canceling (e.g., blocking) any checkpoint operationsinitiated by the first windowed stream operator with respect to thesecond portion of the first subset of the set of stream computing data.In this way, checkpointing may be performed with respect to the set ofstream computing data without a redundant checkpoint. Other methods ofpreventing checkpointing using the first windowed stream operator arealso possible.

At block 988, the set of stream computing data may be checkpointed usingthe second windowed stream operator to checkpoint a second portion ofthe first subset of the set of stream computing data. The checkpointingmay be performed with respect to the stream computing environment.Generally, checkpointing can include recording, saving, logging,preserving, storing, maintaining, or otherwise retaining the set ofstream computing data using the second windowed stream operator tocheckpoint a second portion of the first subset of the set of streamcomputing data. The second portion may include a part, segment, section,or fraction of the first subset of the set of stream computing data. Inembodiments, the second portion may include a segment of the streamcomputing data that is exclusively used by (e.g., unique to, particularto) the second windowed stream operator (e.g., not shared by otherwindowed stream operators). In embodiments, the second portion maydiffer from (e.g., not overlap with, be mutually exclusive with respectto) the first portion of the first subset of the set of stream computingdata. As an example, in a situation in which the set of stream computingdata relates to an Internet-of-Things environment, the second portionmay include a subset of the set of stream computing data that includesair pressure measurements that are marked for sorting by a secondwindowed stream operator of a sorting operator. In embodiments,checkpointing the second portion of the set of stream computing datausing the second windowed stream operator may include configuring thesecond windowed stream operator to parse the set of stream computingdata stored in one or more windows to identify the second portion of thefirst subset of the set of stream computing data (e.g., tuples markedfor processing by the second windowed stream operator), and subsequentlywriting the second portion to set of designated memory addresses forstorage. Other methods of checkpointing the set of stream computing datausing the second windowed stream operator to checkpoint a second portionof the first subset of the set of stream computing data are alsopossible.

In embodiments, the second windowed stream operator may be preventedfrom checkpointing the first portion of the first subset of the set ofstream computing data at block 989. The preventing may be performed withrespect to the stream computing environment. Generally, preventing caninclude limiting, blocking, restricting, forbidding, controlling, orotherwise regulating checkpointing of the second portion of the firstsubset of the set of stream computing data by the second windowed streamoperator. In embodiments, preventing may include locking the firstsubset of the set of stream computing data with respect to the secondwindowed stream operator. For instance, in certain embodiments, lockingmay include establishing a read-operation lock, a write-operation lock,or both with respect to the second subset of the set of stream computingdata to prohibit read or write access to the first subset of the set ofstream computing data by the second windowed stream operator. In certainembodiments, preventing may include locking one or more memory spaces todisallow the second windowed stream operator from carrying-out acheckpointing operation of the first subset of the set of streamcomputing data. Other methods of preventing checkpointing of the firstsubset of the set of stream computing data by the second windowed streamoperator are also possible.

Method 900 concludes at block 999. Aspects of method 900 may provideperformance or efficiency benefits related to checkpointing a set ofstream computing data. Altogether, leveraging non-redundantcheckpointing with respect to shared data of a set of windowed streamoperators may be associated with benefits such as data storageefficiency, bandwidth, and stream computing application performance.Aspects may save resources such as bandwidth, disk, processing, ormemory.

FIG. 10 shows an example system 1000 for checkpointing a set of streamcomputing data with respect to a stream computing environment having aset of windowed stream operators including both a first windowed streamoperator and a second windowed stream operator, according toembodiments. The example system 1000 may include a processor 1006 and amemory 1008 to facilitate implementation of checkpointing a set ofstream computing data. The example system 1000 may include a database1002 (e.g., checkpointing database). In embodiments, the example system1000 may include a checkpointing system 1010. The checkpointing system1010 may be communicatively connected to the database 1002, and beconfigured to receive data 1004 related to checkpointing. Thecheckpointing system 1010 may include an identifying module 1020 toidentify that the first windowed stream operator has a first subset, anidentifying module 1040 to identify that the second windowed streamoperator has the first subset, a determining module 1060 to determine tocheckpoint the first subset, and a checkpointing module 1080 tocheckpoint the set of stream computing data. The checkpointing system1010 may be communicatively connected with a module management system1015 that includes one or more modules for implementing aspects ofcheckpointing a set of stream computing data.

In embodiments, it may be detected that the first windowed streamoperator is hosted by a first compute node at module 1022. The detectingmay be performed with respect to the stream computing environment. Itmay be detected that the second windowed stream operator is hosted by asecond compute node. The detecting may be performed with respect to thestream computing environment. Aspects of the disclosure relate to therecognition that, in some situations, one or more of the set of windowedstream operators may be located on separate computing nodes.Accordingly, aspects of the disclosure relate to checkpointing a set ofstream computing data for windowed stream operators on separate computenodes. Generally, detecting can include sensing, discovering,collecting, recognizing, distinguishing, generating, obtaining,ascertaining, or otherwise determining that the first windowed streamoperator is hosted by a first compute node and the second windowedstream operator is hosted by a second compute node. The first and secondcompute nodes may include physical or virtual computing environmentsconfigured to maintain, support, and facilitate performance of one ormore stream operators of a stream computing application. In embodiments,the first and second compute nodes may include separate physicalservers. In certain embodiments, the first and second compute nodes mayinclude virtual machines or virtualized containers located ondistributed hosts. As described herein, aspects of the disclosure relateto detecting that the first windowed stream operator is hosted by afirst compute node and detecting that the second windowed streamoperator is hosted by a second compute node. In embodiments, detectingmay include analyzing a network topology map for the stream computingenvironment and identifying that the first windowed stream operator ismaintained on the first compute node and that the second windowed streamoperator is maintained on a second compute node. As described herein,aspects of the disclosure relate to determining a checkpointingprocedure based on the placement arrangement of the set of windowedstream operators (e.g., placement on different compute nodes may affectthe performance characteristics, bandwidth requirements, or otherfactors). Other methods of detecting that the first windowed streamoperator is hosted by the first compute node and detecting that thesecond windowed stream operator is hosted by the second compute node arealso possible.

In embodiments, it may be detected that the first windowed streamoperator is in a first consistent region at module 1023. The detectingmay be performed with respect to the stream computing environment. Itmay be detected that the second windowed stream operator is in a secondconsistent region. The detecting may be performed with respect to thestream computing environment. Generally, detecting can include sensing,discovering, collecting, recognizing, distinguishing, generating,obtaining, ascertaining, or otherwise determining that the firstwindowed stream operator is in a first consistent region and that thesecond windowed stream operator is in a second consistent region. Thefirst and second consistent regions may include subgraphs (e.g., areas,portions, regions) of a stream computing environment configured toreduce data loss as a result of software errors events and hardwarefailure. The first and consistent regions may be configured to processeach tuple within the subgraph at least once (e.g., at least-onceprocessing guarantee), such that tuples that exit the consistent regionmay be associated with new operating behavior (e.g., as established byone or more operators of the consistent region). The first consistentregion may differ from the second consistent region. For instance, inembodiments, the first consistent region or the second consistent regionmay differ with respect to at least one stream operator (e.g., the firstconsistent region has a stream operator not included in the secondconsistent region). In certain embodiments, the first consistent regionand the second consistent region may be mutually exclusive (e.g., nooverlap). In embodiments, detecting may include using the streamsmanagement engine to parse an operating graph for the stream computingenvironment and ascertain that the first windowed stream operator isdeployed in the first consistent region and the second windowed streamoperator is deployed in the second consistent region. Other methods ofdetecting that the first windowed stream operator is in the firstconsistent region and the second windowed stream operator is in thesecond consistent region are also possible.

In embodiments, the set of stream computing data may be checkpointed ina Redis database at module 1082. Generally, checkpointing can includerecording, saving, logging, preserving, storing, maintaining, orotherwise retaining the set of stream computing data in a Redisdatabase. The Redis database may include a key-value database forstoring, retrieving, and managing data in a set of associative arrays(e.g., hashes). The set of associative arrays may include a collectionof data objects (e.g., records) that have one or more fields for datastorage. Data objects may be stored and retrieved using keys thatuniquely identify particular data objects within the array. Inembodiments, checkpointing may include receiving a submission of a setof stream computing data from one or more windowed stream operators, andassigning one or more keys to subsets of the set of stream computingdata (e.g., based on data type). The assigned keys may be used toorganize and store the set of stream computing data in one or moreassociative arrays of the Redis database. In embodiments, the Redisdatabase may be configured to hold the stored set of stream computingdata in main memory. In certain embodiments, the Redis database may beconfigured to hold the stored set of stream computing data in virtualmemory such that a portion of the set of stream computing data is savedto a non-volatile storage device (e.g., hard disk). In certainembodiments, the Redis database may be configured to capture a snapshotof the set of stream computing data (e.g., periodically transferringdata between memory and disk in an asynchronous fashion) to promotepersistent availability of the set of stream computing data (e.g., toallow for retrieval, rebuilding stream operators). Other methods ofcheckpointing the set of stream computing data in the Redis database arealso possible.

In embodiments, a first rebuilt windowed stream operator may beassembled at module 1083. The assembling may be performed based on andin response to checkpointing the set of stream computing data withoutthe redundant checkpoint of the first subset of the set of streamcomputing data related to the first and second windowed streamoperators. A second rebuilt windowed stream operator may be assembled.The assembling may be performed based on and in response tocheckpointing the set of stream computing data without the redundantcheckpoint of the second subset of the set of stream computing datarelated to the first and second windowed stream operators. Aspects ofthe disclosure relate to the recognition that, in some situations, itmay be desirable to retrieve a portion of the set of stream computingdata that has been checkpointed. Accordingly, aspects of the disclosurerelate to assembling rebuilt windows for the first and second windowedstream operators using the checkpointed set of stream computing data.Generally, assembling can include building, creating, constructing,putting-together, forming, establishing, or otherwise structuring thefirst rebuilt windowed stream operator and the second rebuilt windowedstream operator. The first and second rebuilt windowed stream operatorsmay include stream operators that substantially correspond to the stateof the first and second windowed stream operators, respectively, beforecheckpointing of the set of stream computing data. In embodiments, thefirst and second rebuilt windowed stream operators may be associatedwith the same stream operator windows, window sizes, operatorfunctionality, access privileges, and stream computing data as beforeperformance of the checkpointing operation. In embodiments, assemblingmay include fetching the first and second subsets of the set of streamcomputing data from a designated memory resource (e.g., where it wascheckpointed), and generating the first and second rebuilt windowedstream operators based on the first and second subsets of the set ofstream computing data, respectively. As an example, consider a firstwindowed stream operator including a filter operator that is associatedwith a window size of 2 gigabytes and a first subset of the set ofstream computing data, and a second windowed stream operator including ajoin operator that is associated with a window size of 1.4 gigabytes anda second subset of the set of stream computing data. As describedherein, assembling may include retrieving the first and second subsetsof the set of stream computing data from the designated memory resourcewhere they were checkpointed, and creating a first rebuilt windowedstream operator that has a window size of 2 gigabytes and is associatedwith the first subset of the set of stream computing data and a secondrebuilt windowed stream operator that has a window size of 1.4 gigabytesand is associated with the second subset of the set of stream computingdata. In certain embodiments, assembling the first and second rebuiltwindowed stream operators may be performed in response to detecting atrigger event with respect to the stream computing environment (e.g.,error or malfunction, network topology change, hostpool configurationchange, operator fusion event). As such, the stream computingenvironment may roll-back to (e.g., return to, revert to) a previousstate based on the checkpointed set of stream computing data. Othermethods of assembling the first and second rebuilt windowed streamoperators based on and in response to checkpointing the set of streamcomputing data are also possible.

Consider the following example. A stream computing environment may beanalyzed using a streams management engine, and it may be identifiedthat a stream computing application includes a first group of severalhundred stream operators hosted on a first compute node, and a secondgroup of several hundred stream operators on a second compute node. Inembodiments, the second group of stream operators may be located in aconsistent region (e.g., such that the entire consistent region must bestored when checkpointing is performed), while the first group of streamoperators correspond to a FileSource (e.g., where small sets of streamcomputing data are checkpointed). As described herein, the streamsmanagement engine may identify that the first group of stream operatorsand the second group of operators share a first subset of streamcomputing data related to a social media environment. Based on the firstand second groups of stream operators having the same first subset ofstream computing data, it may be determined to checkpoint the firstsubset of the set of stream computing data. In embodiments, as describedherein, independent checkpoint procedures may be generated for the firstand second groups of stream operators based on the host characteristicsfor each group. For instance, in embodiments, a first checkpointprocedure for the first group of stream operators may be ascertained inwhich a designated subset of the first group of stream operatorscarries-out checkpoint operations for the first group (e.g., as theamount of data to be checkpointed is small, checkpoint operations may behandled by a few stream operators without impacting system performance).As another example, a second checkpoint procedure for the second groupof stream operators may be ascertained in which each stream operator ofthe second group of stream operators individually performs checkpointoperations for a non-overlapping subset of the set subset of streamcomputing data (e.g., as the entire consistent region must becheckpointed, handling checkpoint operations on an operator-level basismay be associated with efficient load balancing). As such, the first andsecond checkpoint procedures may be performed to store the first subsetof the set of stream computing data to a Redis server. In the event of atriggering event (e.g., error or malfunction, network topology change,hostpool configuration change, operator fusion event), the first subsetof the set of stream computing data may be retrieved from the Redisserver to assemble one or more rebuilt windowed stream operators. Othermethods of managing checkpointing with respect to a stream computingenvironment are also possible.

FIG. 11 is a flowchart illustrating a method 1100 for checkpointing aset of stream computing data with respect to a stream computingenvironment having a set of windowed stream operators including both afirst windowed stream operator and a second windowed stream operator,according to embodiments. Aspects of method 1100 may be similar or thesame as method 600/700/800/900/1000, and aspects may be utilizedinterchangeably. The method 1100 may begin at block 1101. At block 1120,it may be identified that the first windowed stream operator has a firstsubset of the set of stream computing data. The identifying may beperformed with respect to the stream computing environment. At block1140, it may be identified that the second windowed stream operator hasthe first subset of the set of stream computing data. The identifyingmay be performed with respect to the stream computing environment. Atblock 1160, it may be determined to checkpoint the first subset of theset of stream computing data without a redundant checkpoint related tothe first and second windowed stream operators. The determining may beperformed based on both the first and second windowed stream operatorshaving the first subset of the set of stream computing data. At block1180, the set of stream computing data may be checkpointed. Thecheckpointing may be performed without the redundant checkpoint of thefirst subset of the set of stream computing data related to the firstand second windowed stream operators. The checkpointing may be performedwith respect to the stream computing environment.

At block 1191, the stream of tuples may be received to be processed by aset of processing elements (e.g., stream operators) which operates on aset of compute nodes (e.g., in a stream application environment). Thestream of tuples may be received consistent with the description hereinincluding FIGS. 1-14. Current/future processing by the plurality ofprocessing elements may be performed consistent with the descriptionherein including FIGS. 1-14. The set of compute nodes may include ashared pool of configurable computing resources. For example, the set ofcompute nodes can include a public cloud environment, a private cloudenvironment, a distributed batch data processing environment, or ahybrid cloud environment. In certain embodiments, each of the set ofcompute nodes are physically separate from one another.

At block 1192, the stream of tuples may be processed using the set ofprocessing elements operating on the set of compute nodes. The stream oftuples may be processed by the plurality of processing elementsoperating on the set of compute nodes. The stream of tuples may beprocessed consistent with the description herein including FIGS. 1-14.In embodiments, stream operators operating on the set of compute nodesmay be utilized to process the stream of tuples. Processing of thestream of tuples by the plurality of processing elements may providevarious flexibilities for stream operator management. Overall flow(e.g., data flow) may be positively impacted by utilizing the streamoperators.

Method 1100 concludes at block 1199. Aspects of method 1100 may provideperformance or efficiency benefits related to checkpointing a set ofstream computing data. Altogether, leveraging non-redundantcheckpointing with respect to shared data of a set of windowed streamoperators may be associated with benefits such as data storageefficiency, bandwidth, and stream computing application performance.Aspects may save resources such as bandwidth, disk, processing, ormemory.

FIG. 12 illustrates an example 1200 for checkpointing a set of streamcomputing data with respect to a stream computing environment having aset of windowed stream operators including both a first windowed streamoperator 1201 and a second windowed stream operator 1203, according toembodiments. Aspects of the example 1200 relate to the recognition that,in some situations, checkpoints (e.g., aggregates) may be periodicallycreated for stream operators to save the state of an operator graph. Forinstance, as shown in FIG. 12, a first checkpoint 1210 may be createdafter an hour, a second checkpoint 1220 may be created after 12 hours, athird checkpoint 1230 may be created after 1 day, and a fourthcheckpoint 1240 may be created after 1 week. As described herein, insome situations, the stream computing data saved by one checkpoint maybe redundant with respect to the stream computing data saved by anothercheckpoint. As an example, the third checkpoint 1230 may include sets ofstream computing data for both the first checkpoint 1210, the secondcheckpoint 1220, as well as new data collected since the time of thesecond checkpoint 1220, such that the data for the third checkpoint 1230may overlap with data saved by previous checkpoints. Accordingly, asdescribed herein, aspects of the disclosure relate to checkpointing aset of stream computing data in a non-redundant fashion (e.g., topromote stream computing environment efficiency).

FIG. 13 illustrates an example 1300 for checkpointing a set of streamcomputing data with respect to a stream computing environment having aset of windowed stream operators including both a first windowed streamoperator and a second windowed stream operator, according toembodiments. Aspects of the example 1300 relate to the recognition that,in some embodiments, the set of stream computing data may includeoverlapping data from a plurality of stream computing operators. Forinstance, consider a stream computing environment including threewindowed stream computing operators A, B, and C. As shown in FIG. 13, afirst set of stream computing data 1310 may include overlapping data forall three windowed stream computing operators A, B, and C. The first setof stream computing data 1310 may include a first stream computing datasubset 1320 that includes overlapping data from operators A and B, asecond stream computing data subset 1330 that includes data unique tooperator A, a third stream computing data subset 1340 that includes dataunique to operator B, and a fourth stream computing data subset 1350that includes data unique to operator C. As described herein, aspects ofthe disclosure relate to performing checkpoint operations for the threewindowed stream computing operators A, B, and C without redundantcheckpointing. For instance, in embodiments, a particular streamoperator (e.g., operator A) may be designated to checkpoint the set ofstream computing data 1310 for all three of the windowed streamcomputing operators to eliminate the need for redundant checkpointing byother stream operators (e.g., operator B or C). As another example,checkpoint operations may be divided between each of the three windowedstream computing operators to facilitate workload and bandwidthbalancing in the stream computing environment. For instance, streamoperator A may be configured to manage checkpointing of the secondstream computing data subset 1330, stream operator B may be configuredto manage checkpointing of the third stream computing data subset 1340,and stream operator C may be configured to manage checkpointing of thefourth stream computing data subset 1350 (e.g., each windowed streamoperator performs checkpointing for its own data to eliminateredundancy). Other methods of checkpointing stream computing data in anon-redundant fashion are also possible.

FIG. 14 illustrates an example 1400 for checkpointing a set of streamcomputing data with respect to a stream computing environment having aset of windowed stream operators including both a first windowed streamoperator and a second windowed stream operator, according toembodiments. Aspects of the example 1400 relate to assembling a rebuiltwindowed stream operator based on a checkpointed set of stream computingdata. Consider once again a stream computing environment including threewindowed stream computing operators A, B, and C. In certain embodiments,one or more rebuilt windowed stream operators may be assembled usedcheckpointed data. For instance, in certain embodiments, a streamsmanagement engine of the stream computing environment may be configuredto retrieve a first subset 1420 of the set of stream computing datacorresponding to stream computing data for operators A and B and asecond subset 1430 of the set of stream computing data corresponding tostream computing data for operator C from a designated memory resource.Accordingly, the first subset 1420 may be combined with the secondsubset 1430 to assemble a set of stream computing data 1410 for arebuilt windowed stream operator. In this way, the checkpointed set ofstream computing data may be used to roll-back to (e.g., return to,revert to) a previous state based on the checkpointed set of streamcomputing data. Other methods of assembling rebuilt windowed streamoperators based on checkpointed data are also possible.

In addition to embodiments described above, other embodiments havingfewer operational steps, more operational steps, or differentoperational steps are contemplated. Also, some embodiments may performsome or all of the above operational steps in a different order. Inembodiments, operational steps may be performed in response to otheroperational steps. The modules are listed and described illustrativelyaccording to an embodiment and are not meant to indicate necessity of aparticular module or exclusivity of other potential modules (orfunctions/purposes as applied to a specific module).

In the foregoing, reference is made to various embodiments. It should beunderstood, however, that this disclosure is not limited to thespecifically described embodiments. Instead, any combination of thedescribed features and elements, whether related to differentembodiments or not, is contemplated to implement and practice thisdisclosure. Many modifications and variations may be apparent to thoseof ordinary skill in the art without departing from the scope and spiritof the described embodiments. Furthermore, although embodiments of thisdisclosure may achieve advantages over other possible solutions or overthe prior art, whether or not a particular advantage is achieved by agiven embodiment is not limiting of this disclosure. Thus, the describedaspects, features, embodiments, and advantages are merely illustrativeand are not considered elements or limitations of the appended claimsexcept where explicitly recited in a claim(s).

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

Embodiments according to this disclosure may be provided to end-usersthrough a cloud-computing infrastructure. Cloud computing generallyrefers to the provision of scalable computing resources as a serviceover a network. More formally, cloud computing may be defined as acomputing capability that provides an abstraction between the computingresource and its underlying technical architecture (e.g., servers,storage, networks), enabling convenient, on-demand network access to ashared pool of configurable computing resources that can be rapidlyprovisioned and released with minimal management effort or serviceprovider interaction. Thus, cloud computing allows a user to accessvirtual computing resources (e.g., storage, data, applications, and evencomplete virtualized computing systems) in “the cloud,” without regardfor the underlying physical systems (or locations of those systems) usedto provide the computing resources.

Typically, cloud-computing resources are provided to a user on apay-per-use basis, where users are charged only for the computingresources actually used (e.g., an amount of storage space used by a useror a number of virtualized systems instantiated by the user). A user canaccess any of the resources that reside in the cloud at any time, andfrom anywhere across the Internet. In context of the present disclosure,a user may access applications or related data available in the cloud.For example, the nodes used to create a stream computing application maybe virtual machines hosted by a cloud service provider. Doing so allowsa user to access this information from any computing system attached toa network connected to the cloud (e.g., the Internet).

Embodiments of the present disclosure may also be delivered as part of aservice engagement with a client corporation, nonprofit organization,government entity, internal organizational structure, or the like. Theseembodiments may include configuring a computer system to perform, anddeploying software, hardware, and web services that implement, some orall of the methods described herein. These embodiments may also includeanalyzing the client's operations, creating recommendations responsiveto the analysis, building systems that implement portions of therecommendations, integrating the systems into existing processes andinfrastructure, metering use of the systems, allocating expenses tousers of the systems, and billing for use of the systems.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

While the foregoing is directed to exemplary embodiments, other andfurther embodiments of the invention may be devised without departingfrom the basic scope thereof, and the scope thereof is determined by theclaims that follow. The descriptions of the various embodiments of thepresent disclosure have been presented for purposes of illustration, butare not intended to be exhaustive or limited to the embodimentsdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art without departing from the scope and spiritof the described embodiments. The terminology used herein was chosen toexplain the principles of the embodiments, the practical application ortechnical improvement over technologies found in the marketplace, or toenable others of ordinary skill in the art to understand the embodimentsdisclosed herein.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the variousembodiments. As used herein, the singular forms “a,” “an,” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. “Set of,” “group of,” “bunch of,” etc. are intendedto include one or more. It will be further understood that the terms“includes” and/or “including,” when used in this specification, specifythe presence of the stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof. In the previous detaileddescription of exemplary embodiments of the various embodiments,reference was made to the accompanying drawings (where like numbersrepresent like elements), which form a part hereof, and in which isshown by way of illustration specific exemplary embodiments in which thevarious embodiments may be practiced. These embodiments were describedin sufficient detail to enable those skilled in the art to practice theembodiments, but other embodiments may be used and logical, mechanical,electrical, and other changes may be made without departing from thescope of the various embodiments. In the previous description, numerousspecific details were set forth to provide a thorough understanding thevarious embodiments. But, the various embodiments may be practicedwithout these specific details. In other instances, well-known circuits,structures, and techniques have not been shown in detail in order not toobscure embodiments.

What is claimed is:
 1. A computer-implemented method for checkpointing aset of stream computing data with respect to a stream computingenvironment having a set of windowed stream operators including both afirst windowed stream operator and a second windowed stream operator,the method comprising: identifying in real time, with respect to thestream computing environment, that the first windowed stream operatorhas a first subset of the set of stream computing data, wherein thefirst windowed stream operator includes a filter operator associatedwith a common window that stores incoming tuples prior to filtering;identifying, with respect to the stream computing environment, that thesecond windowed stream operator has the first subset of the set ofstream computing data, wherein the second windowed stream operatorincludes a sort operator associated with the common window that storesincoming tuples prior to sorting, the common window shared by both thefirst and second windowed stream operators; determining, based on boththe first and second windowed stream operators having the first subsetof the set of stream computing data, to checkpoint the first subset ofthe set of stream computing data without a redundant checkpoint relatedto the first and second windowed stream operators; checkpointing, withrespect to the stream computing environment, the set of stream computingdata without the redundant checkpoint of the first subset of the set ofstream computing data related to the first and second windowed streamoperators; detecting a first throughput factor for the first windowedstream operator, the first throughput factor including an indication ofthe rate at which data is processed by the first windowed streamoperator; detecting a second throughput factor for the second windowedstream operator, the second throughput factor including an indication ofthe rate at which data is processed by the second windowed streamoperator; comparing the first and second throughput factors for thefirst and second windowed stream operators; determining that the firstthroughput factor exceeds the second throughput factor; in response todetermining that the first throughput factor exceeds the secondthroughput factor checkpointing, with respect to the stream computingenvironment, the set of stream computing data using the first windowedstream operator to checkpoint the first subset of the set of streamcomputing data and preventing, with respect to the stream computingenvironment, the second windowed stream operator from checkpointing thefirst subset of the set of stream computing data; wherein the methodfurther comprises: identifying, with respect to the stream computingenvironment, that the first windowed stream operator has a second subsetof the set of stream computing data; identifying, with respect to thestream computing environment, that the second windowed stream operatorhas a third subset of the set of stream computing data; checkpointing,with respect to the stream computing environment, the set of streamcomputing data using the first windowed stream operator to checkpointthe second subset of the set of stream computing data; andcheckpointing, with respect to the stream computing environment, the setof stream computing data using the second windowed stream operator tocheckpoint the third subset of the set of stream computing data.